Thesis
Supporting Distributed Machine Learning in Heterogeneous and Dynamic Environment
Washington State University
Master of Science (MS), Washington State University
2023
DOI:
https://doi.org/10.7273/000006360
Abstract
With the growing popularity demand of Internet-of-Things applications, there is high interest to support machine learning workflows using heterogeneous edge devices, i.e., in a distributed system. Nonetheless, achieving better computational performance without sacrificing the accuracy in distributed machine learning is challenging, due to the complexity and reliability of distributed system. In this study we provide support towards distributed machine learning in heterogeneous and dynamic environment.
We have successfully developed an adaptive algorithm which enhances computational performance in distributed machine learning to overcome these impediments. The edge devices were dynamically configured with different number of epoch depending on its execution training time from the previous round. Our analysis showed promising performance of the adaptive algorithm with respect to better execution time without sacrificing the accuracy compared to the federated averaging algorithm.
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Details
- Title
- Supporting Distributed Machine Learning in Heterogeneous and Dynamic Environment
- Creators
- Mohd Shafiq Azizan
- Contributors
- Xinghui Zhao (Advisor)Xuechen Zhang (Committee Member)Scott Wallace (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- School of Engineering and Computer Science (VANC)
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 64
- Identifiers
- 99901087338301842
- Language
- English
- Resource Type
- Thesis